In general, instruments for performing surface property measurements on specimens, typically referred to as “indentation” tests, are commonly used in many laboratories. Specifically, workpiece hardness (defined as the force required to deform an area, H=F/A) is used to characterize a specimen's resistance to both plastic and, in some cases, elastic deformation. Workpiece hardness is one of the key parameters for determining the mechanical/tribological characteristics of a workpiece and once workpiece hardness is determined, other material properties, such as modulus and yield strength, are easier to characterize. Specimens are measured to ascertain their present hardness values, and the related manufacturing processes are adjusted to adhere to specification requirements. In other words, hardness values are often used to maintain the quality of parts produced via manufacturing processes. The characterization of material properties on the nanoscale is critical in the thin films industry, for example (i.e. with respect to tool coatings, adhesives, micro-electro-mechanical systems (MEMS) semiconductors, chemo-mechanical polishing, etc.). The forces applied are typically less than about 40 mN and the indentation depths induced are typically less than about 200 nm (i.e. nanoscale surface property measurements are typically non-destructive).
In order to adhere to increasingly tight specification requirements, accurate, reliable, and traceable surface property measurements are required. A major contributor to uncertainties in these surface property measurements is instrument frame and stage distortion. Some contributors to instrument frame and stage distortion are static and dynamic loading, thermal effects, material stability, and mechanical hysteresis. Most of these influences are neither linear, repeatable, nor reversible, and are thus difficult to deconvolve.
In order to account for the above-referenced influences, conventional instruments for performing surface property measurements have attempted to characterize, predict, and compensate for instrument frame and stage distortion. Specifically, this has been done under load, using an amorphous material of known hardness to characterize the stiffness of the instrument frame and stage. Deviations from a known measurement have been deemed representative of instrumental effects. This is a particularly important procedure for calibrating micro and nano instruments that depend on the simultaneous and coincident measurement of forces and relative motions transmitted around the instruments. Unfortunately, the result is often corrected measurements that are not independent of scale, resulting in large uncertainties for measurements that differ significantly from those experienced during the characterization process. Thus, conventional characterization processes do not adequately detect or compensate for non-linear, non-repeatable, or time-dependent influences.
In addition to resulting in size-dependent measurements, calibration using an amorphous material (i.e. the specimen itself) inherently changes the stiffness of the instrument frame and stage, adding further uncertainty to the calibration process. The stiffness of the amorphous material is typically different from the stiffness of the specimen, for example.
In order to measure the depth of penetration of a stylus into the surface of a specimen, conventional instruments for performing surface property measurements use a combination of distortion calibration and the calculation of the displacement of the related force sensor (also referred to as the “load cell”) used to measure the applied load. In addition to the above-referenced uncertainties associated with the distortion calibration, there are additional uncertainties in deconvolving which displacements in the force sensor are related to distortions and which are related to the depth of penetration of the stylus into the surface of the specimen.
Thus, what are still needed in the art are apparatuses and methods that remove many of the above-referenced uncertainties and minimize the significance of those that remain. Preferably, those uncertainties that remain are of a variety that may be predicted, estimated, and/or compensated for with a relatively high degree of certainty.